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Record W1997956955 · doi:10.1155/wcn/2006/74812

Error Control Coding in Low-Power Wireless Sensor Networks: When Is ECC Energy-Efficient?

2006· article· en· W1997956955 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEURASIP Journal on Wireless Communications and Networking · 2006
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceTransmitterCoding gainCoding (social sciences)Wireless sensor networkEfficient energy useEnergy consumptionEnergy harvestingDecoding methodsTransmitter power outputWirelessImplementationEnergy (signal processing)Real-time computingElectronic engineeringElectrical engineeringTelecommunicationsComputer networkMathematics

Abstract

fetched live from OpenAlex

This paper examines error control coding (ECC) use in wireless sensor networks (WSNs) to determine the energy efficiency of specific ECC implementations in WSNs. ECC provides coding gain, resulting in transmitter energy savings, at the cost of added decoder power consumption. This paper derives an expression for the critical distance , the distance at which the decoder's energy consumption per bit equals the transmit energy savings per bit due to coding gain, compared to an uncoded system. Results for several decoder implementations, both analog and digital, are presented for in different environments over a wide frequency range. In free space, is very large at lower frequencies, suitable only for widely spaced outdoor sensors. In crowded environments and office buildings, drops significantly, to 3 m or greater at 10 GHz. Interference is not considered; it would lower . Analog decoders are shown to be the most energy-efficient decoders in this study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.234
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it